From 23c46830648bb0b855cfcea482f06f16c51d5b7d Mon Sep 17 00:00:00 2001 From: launaflanigan Date: Mon, 17 Feb 2025 13:45:50 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace And Amazon SageMaker JumpStart.-.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md diff --git a/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md new file mode 100644 index 0000000..96140df --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are thrilled to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://git.qoto.org) JumpStart. With this launch, you can now deploy DeepSeek [AI](https://spm.social)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://www.9iii9.com) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://www.jccer.com:2223) that uses support learning to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support knowing (RL) action, which was utilized to fine-tune the design's responses beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down [complex queries](https://snapfyn.com) and reason through them in a detailed way. This guided thinking procedure permits the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be integrated into various workflows such as agents, rational thinking and data interpretation tasks.
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DeepSeek-R1 [utilizes](https://source.ecoversities.org) a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling effective inference by routing queries to the most relevant specialist "clusters." This method allows the design to focus on various issue domains while maintaining overall effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to [release](https://www.bisshogram.com) the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to imitate the behavior and [thinking patterns](http://motojic.com) of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog, [classificados.diariodovale.com.br](https://classificados.diariodovale.com.br/author/tawnyafoti/) we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate models against key security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://social.nextismyapp.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To [examine](https://spm.social) if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and [raovatonline.org](https://raovatonline.org/author/arletha3316/) validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation boost, create a limit boost request and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock [Guardrails](https://git.nothamor.com3000) [permits](https://radiothamkin.com) you to present safeguards, prevent damaging content, and examine designs against essential security criteria. You can implement safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation involves the following steps: First, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:WinstonNajera5) the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the [design's](http://git.zhiweisz.cn3000) output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a [message](https://rsh-recruitment.nl) is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas demonstrate inference [utilizing](https://twoplustwoequal.com) this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, [yewiki.org](https://www.yewiki.org/User:WinifredHassell) emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
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The design detail page [supplies](http://soho.ooi.kr) necessary details about the model's abilities, rates structure, and execution guidelines. You can find detailed use directions, including sample API calls and code snippets for integration. The design supports different text generation tasks, [including](https://git.getmind.cn) content creation, code generation, and question answering, [utilizing](https://www.ministryboard.org) its support finding out optimization and CoT reasoning capabilities. +The page likewise includes release choices and licensing [details](http://www.xn--9m1b66aq3oyvjvmate.com) to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be [triggered](https://git.nagaev.pro) to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of instances (in between 1-100). +6. For example type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you may wish to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change design criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for inference.
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This is an outstanding way to check out the model's thinking and text generation abilities before integrating it into your applications. The play area offers [instant](https://chancefinders.com) feedback, helping you understand how the model responds to various inputs and letting you tweak your triggers for results.
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You can quickly [evaluate](https://gitea.eggtech.net) the model in the [play ground](http://www.hnyqy.net3000) through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, [utilize](https://insta.kptain.com) the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a demand to produce text based upon a user timely.
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Deploy DeepSeek-R1 with [SageMaker](http://8.136.42.2418088) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With [SageMaker](https://newtheories.info) JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical techniques: using the instinctive SageMaker [JumpStart UI](http://wrs.spdns.eu) or executing programmatically through the [SageMaker Python](https://gitlab.xfce.org) SDK. Let's check out both [techniques](https://www.imf1fan.com) to assist you select the method that best matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser displays available designs, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card [reveals essential](http://xintechs.com3000) details, including:
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- Model name +[- Provider](https://gitlab.isc.org) name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the model details page.
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The model details page consists of the following details:
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- The design name and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:CherylCastiglia) supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model [description](https://git.todayisyou.co.kr). +- License details. +- Technical specs. +- Usage standards
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Before you release the design, it's advised to evaluate the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the instantly created name or develop a customized one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is crucial for expense and efficiency optimization. [Monitor](https://chaakri.com) your release to change these [settings](https://rna.link) as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly suggest sticking to SageMaker JumpStart [default](https://git.cocorolife.tw) settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
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The release process can take a number of minutes to complete.
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When implementation is complete, your [endpoint status](https://gitlab.radioecca.org) will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is total, you can conjure up the design using a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is [supplied](https://www.florevit.com) in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional requests against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To [prevent unwanted](https://turizm.md) charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed releases area, locate the endpoint you want to delete. +3. Select the endpoint, and on the [Actions](https://git.chir.rs) menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the right implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://git.swordlost.top) companies build innovative [solutions utilizing](http://gs1media.oliot.org) AWS services and accelerated calculate. Currently, he is [concentrated](http://60.209.125.23820010) on developing strategies for fine-tuning and optimizing the inference performance of big language designs. In his downtime, Vivek enjoys treking, seeing films, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://radiothamkin.com) Specialist Solutions Architect with the [Third-Party Model](https://www.footballclubfans.com) Science team at AWS. His [location](https://fumbitv.com) of focus is AWS [AI](https://careerportals.co.za) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://www.florevit.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.arachno.de) hub. She is enthusiastic about developing services that assist clients accelerate their [AI](https://edge1.co.kr) journey and unlock organization worth.
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